Random effects on numbers-at-age transitions implicitly account for movement dynamics and improve stock assessment and management
Bibliographic record
Abstract
Implementing operational assessment models that account for spatial structure and movement dynamics is challenging, especially with limited tagging data. Random effects on numbers-at-age (NAA) transitions in state–space models offer a potential solution to circumvent direct movement estimation by attributing movement variation to NAA random effects. However, whether this approach reliably achieves desirable management outcomes remains unclear. In this study, we conducted a management strategy evaluation that emulated a generic medium-lived fish that exhibit natal homing dynamics, using assessment models with varying levels of spatial complexity. We compared the performance of each spatial implementation with and without NAA random effects to evaluate their effectiveness in achieving management outcomes. Our results showed that models with NAA random effects consistently outperformed those without, although the benefits of NAA random effects degraded at high rates of movement. Therefore, NAA random effects could serve as a practical intermediate solution when explicit movement modeling is not feasible due to insufficient movement information. Our findings suggest that incorporating NAA random effects should be a default starting point in state–space stock assessments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".